Take Home Exercise 3

Uncover illegal, unreported, and unregulated (IUU) fishing activities through visual analytics

Author

Fangxian

Published

June 4, 2023

Modified

June 18, 2023

Task

Data Wraggling

Load Packages

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pacman::p_load(jsonlite, tidygraph, ggraph, visNetwork, graphlayouts, ggforce,skimr,tidytext, tidyverse,igraph)

Data Import

In the code chunk below, fromJSON() of jsonlite package is used to import MC3.json into R environment.

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mc3_data <- fromJSON("data/MC3.json")

Examine the data, this is not a directed graph, not looking into in- and out-degree of the nodes.

Extracting edges

Below code chunk changes the links field into character field.

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mc3_edges <- as_tibble(mc3_data$links)%>%
  distinct() %>%
  mutate(source = as.character(source),
         target = as.character(target),
         type = as.character(type)) %>%
  group_by(source, target, type) %>%
    summarise(weights = n()) %>%
  filter(source!=target)%>%
  ungroup

Extracting nodes

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mc3_nodes <- as_tibble(mc3_data$nodes) %>%
#  distinct()%>%
  mutate(country = as.character(country),
         id = as.character(id),
         product_services = as.character(product_services),
         revenue_omu = as.numeric(as.character(revenue_omu)),
         type = as.character(type)) %>%
    select(id, country, type, revenue_omu, product_services)

Initial Data Exploration

Exploring the edges dataframe

In the code chunk below, skim() of skimr package is used to display the summary statistics of mc3_edges tibble data frame.

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skim(mc3_edges)
Data summary
Name mc3_edges
Number of rows 24036
Number of columns 4
_______________________
Column type frequency:
character 3
numeric 1
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
source 0 1 6 700 0 12856 0
target 0 1 6 28 0 21265 0
type 0 1 16 16 0 2 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
weights 0 1 1 0 1 1 1 1 1 ▁▁▇▁▁

The report above reveals that there is not missing values in all fields.

In the code chunk below, datatable() of DT package is used to display mc3_edges tibble data frame as an interactive table on the html document.

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DT::datatable(mc3_edges)

counting number of companies a person owns

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ggplot(data = mc3_edges,
       aes(x=type)) +
  geom_bar()

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unique_ids <- unique(mc3_edges$target)
num_unique_ids <- length(unique_ids)
num_unique_ids
[1] 21265
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Noofcompanies <- mc3_edges %>%
  group_by(target, source, type) %>%
  filter(type == "Beneficial Owner") %>%
  summarise(count=n()) %>%
  group_by(target)%>%
  summarise(count=sum(count))

psych::describe(Noofcompanies)
        vars     n   mean      sd median trimmed     mad min   max range skew
target*    1 15305 7653.0 4418.32   7653    7653 5672.43   1 15305 15304 0.00
count      2 15305    1.1    0.40      1       1    0.00   1     9     8 6.28
        kurtosis    se
target*    -1.20 35.71
count      61.69  0.00
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# list <- mc3_edges1 %>%
#   filter(mc3_edges1$count > 1)
# 
# psych::describe(list)
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Noofowners <- mc3_edges %>%
  group_by(source, target, type) %>%
  summarise(count=n()) %>%
  group_by(source)%>%
  summarise(count=sum(count))

psych::describe(Noofowners)
        vars     n    mean      sd median trimmed     mad min   max range  skew
source*    1 12856 6428.50 3711.35 6428.5 6428.50 4765.08   1 12856 12855  0.00
count      2 12856    1.87    3.47    1.0    1.22    0.00   1   120   119 11.36
        kurtosis    se
source*    -1.20 32.73
count     215.82  0.03


Show the code 
list_top_50 <- Noofcompanies %>%
  arrange(desc(count)) %>%
  top_n(50, wt = count) 

ggplot(data = list_top_50, 
       aes(x = reorder(target, -count), y = count)) +
  geom_bar(stat = "identity") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

Exploring the nodes dataframe

Show the code 
skim(mc3_nodes)
Data summary
Name mc3_nodes
Number of rows 27622
Number of columns 5
_______________________
Column type frequency:
character 4
numeric 1
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
id 0 1 6 64 0 22929 0
country 0 1 2 15 0 100 0
type 0 1 7 16 0 3 0
product_services 0 1 4 1737 0 3244 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
revenue_omu 21515 0.22 1822155 18184433 3652.23 7676.36 16210.68 48327.66 310612303 ▇▁▁▁▁

The report above reveals that there is no missing values in all fields.

In the code chunk below, datatable() of DT package is used to display mc3_nodes tibble data frame as an interactive table on the html document.

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DT::datatable(mc3_nodes)
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ggplot(data = mc3_nodes,
       aes(x = type)) +
  geom_bar()

Check on the revenue

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ggplot(data = mc3_nodes,
       aes(x= type,
         y = revenue_omu)) +
  geom_boxplot()

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combined <- left_join(mc3_nodes,mc3_edges,
                  by=c("id"="source"))

Pivot to find out on the revenue fron the target. find out about which target has high revenue

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combined <- combined %>%
  group_by(target, type.y, id, country, type.x, product_services)%>%
  summarize(revenue_omu) %>%
  filter(type.y == "Beneficial Owner")

filtered_combined <- combined %>%
  filter(target %in% list_top_50$target)%>%
  arrange(desc(revenue_omu))

ggplot(data = filtered_combined, 
       aes(x = target, y = revenue_omu)) +
  geom_bar(stat = "identity") +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

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Top_3_Revenue<- combined %>%
  filter (target %in% c("Michael Johnson", "Mark Miller","James Rodriguez")) %>%
  arrange(desc(revenue_omu))

DT::datatable(Top_3_Revenue)

Text Sensing with tidytext

Simple word count

The code chunk below calculates number of times the word fish appeared in the field product_services.

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mc3_nodes %>% 
    mutate(n_fish = str_count(product_services, "fish")) 
# A tibble: 27,622 × 6
   id                          country type  revenue_omu product_services n_fish
   <chr>                       <chr>   <chr>       <dbl> <chr>             <int>
 1 Jones LLC                   ZH      Comp…  310612303. Automobiles           0
 2 Coleman, Hall and Lopez     ZH      Comp…  162734684. Passenger cars,…      0
 3 Aqua Advancements Sashimi … Oceanus Comp…  115004667. Holding firm wh…      0
 4 Makumba Ltd. Liability Co   Utopor… Comp…   90986413. Car service, ca…      0
 5 Taylor, Taylor and Farrell  ZH      Comp…   81466667. Fully electric …      0
 6 Harmon, Edwards and Bates   ZH      Comp…   75070435. Discount superm…      0
 7 Punjab s Marine conservati… Riodel… Comp…   72167572. Beef, pork, chi…      0
 8 Assam   Limited Liability … Utopor… Comp…   72162317. Power and Gas s…      0
 9 Ianira Starfish Sagl Import Rio Is… Comp…   68832979. Light commercia…      0
10 Moran, Lewis and Jimenez    ZH      Comp…   65592906. Automobiles, tr…      0
# ℹ 27,612 more rows

Tokenisation

The word tokenisation have different meaning in different scientific domains. In text sensing, tokenisation is the process of breaking up a given text into units called tokens. Tokens can be individual words, phrases or even whole sentences. In the process of tokenisation, some characters like punctuation marks may be discarded. The tokens usually become the input for the processes like parsing and text mining.

In the code chunk below, unnest_token() of tidytext is used to split text in product_services field into words.

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token_nodes <- mc3_nodes %>%
  unnest_tokens(word, 
                product_services)

The two basic arguments to unnest_tokens() used here are column names. First we have the output column name that will be created as the text is unnested into it (word, in this case), and then the input column that the text comes from (product_services, in this case).

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token_nodes %>%
  count(word, sort = TRUE) %>%
  top_n(15) %>%
  mutate(word = reorder(word, n)) %>%
  ggplot(aes(x = word, y = n)) +
  geom_col() +
  xlab(NULL) +
  coord_flip() +
      labs(x = "Count",
      y = "Unique words",
      title = "Count of unique words found in product_services field")

The bar chart reveals that the unique words contains some words that may not be useful to use. For instance “a” and “to”. In the word of text mining we call those words stop words. You want to remove these words from your analysis as they are fillers used to compose a sentence.

Using filter we also discover many “character(0)” which has no meaning in itself, we will also proceed to replace them with “NA”.

Removing stopwords

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token_nodes$word[token_nodes$word == "character"] <- "NA"
token_nodes$word[token_nodes$word == "0"] <- "NA"
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stopwords_removed <- token_nodes %>% 
  anti_join(stop_words)
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stopwords_removed %>%
  count(word, sort = TRUE) %>%
  top_n(15) %>%
  mutate(word = reorder(word, n)) %>%
  ggplot(aes(x = word, y = n)) +
  geom_col() +
  xlab(NULL) +
  coord_flip() +
      labs(x = "Count",
      y = "Unique words",
      title = "Count of unique words found in product_services field")

Show the code 
stopwords_removed %>%
  count(word, sort = TRUE) %>%
  top_n(20) %>%
  mutate(word = reorder(word, n)) %>%
  filter(!word %in% head(word, 3)) %>%
  ggplot(aes(x = word, y = n)) +
  geom_col() +
  xlab(NULL) +
  coord_flip() +
  labs(x = "Count",
       y = "Unique words",
       title = "Count of unique words found in product_services field")

Initial Network Visualization and Analysis

Building network model with tidygraph

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mc3_nodes_fish <- stopwords_removed %>%
  filter(stopwords_removed$word == "fish")
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mc3_edges_fish <- mc3_edges[mc3_edges$source %in% mc3_nodes_fish$id,]  
id1 <- mc3_edges_fish %>%
  select(source) %>%
  rename(id = source) 
id2 <- mc3_edges_fish %>% 
  select(target) %>% 
  rename(id = target) 
mc3_nodes_fish <- rbind(id1, id2) %>%
  distinct() %>% 
  left_join(mc3_nodes_fish,
            unmatched = "drop")
Show the code 
# mc3_graph <- tbl_graph(nodes = mc3_nodes_fish,                        
#                        edges = mc3_edges_fish,                        
#                        directed = FALSE)  mc3_graph

Topic Modelling